Visualizing regions of conserved synteny between two genomes is supported by numerous software applications. However, none of the current applications allow researchers to select genome features to display or highlight in blocks of synteny based on the annotated biological properties of the features (e.g., type, function, and/or phenotype association). To address this usability gap, we developed an interactive web-based conserved synteny browser, The Jackson Laboratory (JAX) Synteny Browser. The browser allows researchers to highlight or selectively display genome features in the reference and/or the comparison genome according to the biological attributes of the features. Although the current implementation for the browser is limited to the reference genomes for the laboratory mouse and human, the software platform is intentionally genome agnostic. The JAX Synteny Browser software can be deployed for any two genomes where genome coordinates for syntenic blocks are defined and for which biological attributes of the features in one or both genomes are available in widely used standard bioinformatics file formats. The JAX Synteny Browser is available at: http://syntenybrowser.jax.org/ . The code base is available from GitHub: https://github.com/TheJacksonLaboratory/syntenybrowser and is distributed under the Creative Commons Attribution license (CC BY).

BackgroundPatient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison.ResultsWe report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA).ConclusionsThe analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.

Abstract The Jackson Laboratory has established more than 400 unique patient-derived xenograft (PDX) cancer models from patient tumors in the immunocompromised NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (aka, NSGTM) mouse strain, spanning across more than 30 tumor types. At low passages, these engrafted models are known to retain similar molecular characteristics and heterogeneity to the originating human tumor. As such, PDX models offer an excellent preclinical platform to test drug responses of novel cancer therapeutics and a powerful resource for conducting preclinical cancer pharmacogenomic studies. To aid the selection of suitable PDX models for preclinical studies and for the research purpose to understand tumor biology and response or resistance to a given treatment, we have characterized the PDX models for their transcriptomic, mutational and copy number profiles using sequencing and array approaches. We have established a compendium of PDX-tailored computational pipelines as the analysis of genomic data from PDX models could be challenging due to a) the contamination of PDX sample with mouse stroma, which complicates downstream bioinformatics analyses as mouse genome is almost 90% homologous to the human genome, and b) the lack of matched normal material to call somatic events. Our pipelines incorporate various filters to identify tumor specific single nucleotide variants, indels, copy number changes and expression profile in the PDX model. For the purpose of validating the accuracy of our analysis pipelines and demonstrating that the JAX PDX models are indeed representative of patient tumors, we compared JAX’s PDX cohort with patient cohorts in TCGA for mutations, copy number aberrations and RNA expression concordance. Using gene sets representative of each tumor type, we found that the overall genomic profile of each PDX tumor type is more correlated to the same tumor type in TCGA than other tumor types. In addition, an integrative analysis across all data types reveals that there are more common affected pathways between the same tumor type in PDX and TCGA. This comprehensive analysis revealed that the PDX and patient cohorts exhibit similar molecular characteristics, hence establishing the suitability of JAX PDX models as in vivo models to study fundamental tumor biology as well as to carry out preclinical studies of cancer drugs, including identification of biomarkers of response or resistance. Citation Format: Xing Yi Woo, Vinod Yadav, Al Simons, Anuj Srivastava, Guruprasad Ananda, Vishal Kumar Sarsani, Roger Liu, Grace Stafford, Joel Graber, Krishna Karuturi, Susie Airhart, Joshy George, Carol Bult. Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3842. doi:10.1158/1538-7445.AM2017-3842

Background: The continued development of targeted therapeutics for cancer treatment has required the concomitant development of more expansive methods for the molecular profiling of the patient's tumor. We describe the validation of the JAX Cancer Treatment Profile(TM) (JAX-CTP(TM), a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable mutations in solid tumors to inform the selection of targeted therapeutics for cancer treatment.Methods: NGS libraries are generated from DNA extracted from formalin fixed paraffin embedded tumors. Using hybrid capture, the genes of interest are enriched and sequenced on the Illumina HiSeq 2500 or MiSeq sequencers followed by variant detection and functional and clinical annotation for the generation of a clinical report.Results: The JAX-CTP(TM) detects actionable variants, in the form of single nucleotide variations and small insertions and deletions (<= 50 bp) in 190 genes in specimens with a neoplastic cell content of >= 10%. The JAX-CTP(TM) is also validated for the detection of clinically actionable gene amplifications.Conclusions: There is a lack of consensus in the molecular diagnostics field on the best method for the validation of NGS-based assays in oncology, thus the importance of communicating methods, as contained in this report. The growing number of targeted therapeutics and the complexity of the tumor genome necessitate continued development and refinement of advanced assays for tumor profiling to enable precision cancer treatment. (C) 2014 Elsevier Inc. All rights reserved.

Abstract The Jackson Laboratory Cancer Treatment Profile™ (JAX-CTP™) is a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable gene variants in solid tumors to inform the selection of targeted therapeutics for cancer treatment. We will describe the design of the 358- gene panel, analytical validation, and the curation and clinical reporting of actionable variants. Selection of the gene panel was based on known drug targets, casual implications in cancer, and a thorough pathway analysis. DNA is extracted from FFPE tumor samples and using hybrid capture, the genes of interest are enriched and sequenced on Illumina HiSeq 2500 or MiSeq sequencers. FASTQ files generated from Illumina's CASAVA software are submitted to the JAX Clinical Genome Analytics (CGA) data analysis pipeline to perform automated read quality assessment, alignment, and variant calling. Identified variants are then submitted for clinical curation using a combination of the in-house JAX Clinical Knowledgebase (CKB) and the external Genetic Variant Annotation (GVA) from CollabRx. Once clinically annotated, the variants are graded relative to their clinical utility for the specific tumor type and compiled into a clinical report to inform patient treatment. Extensive analytical validation, following CAP guidelines, was conducted to assess limit of detection, accuracy, precision, sensitivity, and specificity of the assay. The summarized optimized sensitivity of the assay is a minimum coverage of samples at 300X, a limit of detection of 10% for SNP’s/indels and ≥6 copies for CNV’s, and an average of 3-4 actionable variants per sample. The challenges of interpreting gene variants for clinical actionability and for establishing an analytically valid bioinformatic pipeline will be discussed in-depth. Citation Format: Susan M. Mockus, Guruprasad Ananda, Micaela Lundquist, Vanessa Spotlow, Al Simons, Talia Mitchell, Grace A. Stafford, Christopher S. Potter, Vivek Philip, Timothy Stearns, Anuj Srivastava, Mary Barter, Lucy Rowe, Joan Malcolm, Carol Bult, Radha Krishna Murthy Katuturi, Karen Rasmussen, Douglas Hinerfeld. Design, validation, and interpretation of an NGS assay for actionable variants in solid tumors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4816. doi:10.1158/1538-7445.AM2015-4816

Background: Vancomycin-resistant Enterococcus faecium (VREf) belonging to clonal cluster 17 (CC17) has been emerging globally since the 1990's and is now among the predominant group of enterococci causing nosocomial infections of the bloodstream, urinary tract, skin and soft-tissues.The aim of this study was to assess the clonality of temporally spaced VREf clinical isolates using whole-genome sequencing analysis.Design: Thirty-four and 113 VREf clinical isolates recovered from patients in a tertiary medical center in suburban New York City in 1995 and 2013, respectively, were selected and analyzed.Whole-genome sequencing was performed on the Illumina MiSeq™ or HiSeq™ System by using paired-end methods.Multilocus sequence typing (MLST) and single nucleotide variations (SNVs) data were derived from the genome sequences of each isolate.The genetic relatedness of different enterococcal isolates and sequence types were explored using the goeBURST program.Results: The predominant strain type for VREf isolates from 1995 was ST17 (79.4%), followed by ST18 (8.8%) and ST323 (8.8%).By contrast, a newly described clone, ST736, accounted for 48.7% of VREf isolates in 2013, followed by ST18 (24.7%) and ST412 (21.2%).Population genetic analysis established that ST736 is a novel clone within CC17 with differences in two alleles from its prototype ST17.Conclusions: Whole-genome sequencing analysis of VREf clinical isolates demonstrated a dramatic change in dominant strain types and the emergence of a novel clone ST736 in our patient population over the past 18 years.

Background: Vancomycin-resistant Enterococcus faecium (VREf) belonging to clonal cluster 17 (CC17) has been emerging globally since the 1990's and is now among the predominant group of enterococci causing nosocomial infections of the bloodstream, urinary tract, skin and soft-tissues.The aim of this study was to assess the clonality of temporally spaced VREf clinical isolates using whole-genome sequencing analysis.Design: Thirty-four and 113 VREf clinical isolates recovered from patients in a tertiary medical center in suburban New York City in 1995 and 2013, respectively, were selected and analyzed.Whole-genome sequencing was performed on the Illumina MiSeq™ or HiSeq™ System by using paired-end methods.Multilocus sequence typing (MLST) and single nucleotide variations (SNVs) data were derived from the genome sequences of each isolate.The genetic relatedness of different enterococcal isolates and sequence types were explored using the goeBURST program.Results: The predominant strain type for VREf isolates from 1995 was ST17 (79.4%), followed by ST18 (8.8%) and ST323 (8.8%).By contrast, a newly described clone, ST736, accounted for 48.7% of VREf isolates in 2013, followed by ST18 (24.7%) and ST412 (21.2%).Population genetic analysis established that ST736 is a novel clone within CC17 with differences in two alleles from its prototype ST17.Conclusions: Whole-genome sequencing analysis of VREf clinical isolates demonstrated a dramatic change in dominant strain types and the emergence of a novel clone ST736 in our patient population over the past 18 years.

A study was conducted in hamsters to determine if group B soyasaponins improve plasma cholesterol status by increasing the excretion of fecal bile acids and neutral sterols, to identify group B soyasaponin metabolites, and to investigate the relationship between a fecal group B soyasaponin metabolite and plasma lipids. Twenty female golden Syrian hamsters, 11-12 weeks old and 85-125 g, were randomly assigned to a control diet or a similar diet containing group B soyasaponins (containing no isoflavones), 2.2 mmol/kg, for 4 weeks. Hamsters fed group B soyasaponins had significantly lower plasma total cholesterol (by 20%), non-high-density lipoprotein (HDL) cholesterol (by 33%), and triglycerides (by 18%) compared with those fed casein (P < 0.05). The ratio of total cholesterol to HDL cholesterol was significantly lower (by 13%) in hamsters fed group B soyasaponins than in those fed casein (P < 0.05). The excretion of fecal bile acids and neutral sterols was significantly greater (by 105% and 85%, respectively) in soyasaponin-fed hamsters compared with those fed casein (P < 0.05). Compared with casein, group B soyasaponins lowered plasma total cholesterol levels and non-HDL cholesterol levels by a mechanism involving greater excretion of fecal bile acids and neutral sterols. Hamsters fed group B soyasaponins statistically clustered into two fecal soyasaponin metabolite-excretion phenotypes: high excreters (n = 3) and low excreters (n = 7). When high and low producers of this soyasaponin metabolite were compared for plasma cholesterol status, the high producers showed a significantly lower total-cholesterol-to-HDL-cholesterol ratio compared with the low producers (1.38 +/- 0.7 vs. 1.59 +/- 0.13; P < 0.03). Greater production of group B soyasaponin metabolite in hamsters was associated with better plasma cholesterol status, suggesting that gut microbial variation in soyasaponin metabolism may influence the health effects of group B soyasaponins.

Gut microbial disappearance and metabolism of the soy isoflavone glycitein, 7,4'-dihydroxy-6-methoxyisoflavone, were investigated by incubating glycitein anaerobically with feces from 12 human subjects. The subjects' ages ranged from 24 to 53 years with a body mass index (BMI) of 20.9-25.8 kg/m(2) (mean BMI = 24.0 +/- 1.1 kg/m(2)). Glycitein disappearance followed an apparent first-order rate loss. Fecal glycitein disappearance rates for the subjects segregated into three different groups described as high (k = 0.67 +/- 0.14/h), moderate (k = 0.34 +/- 0.04/h), and low (k = 0.15 +/- 0.07/h) glycitein degraders (p < 0.0001). There was no dose effect on the disappearance rates for each subject from 10 to 250 microM glycitein (average k = 0.32 +/- 0.03/h, p > 0.05). Four putative glycitein metabolites, characterized by liquid chromatography-mass spectrometry (electrospray ionization using positive ionization mode), were dihydroglycitein, dihydro-6,7,4'-trihydroxyisoflavone, and 5'-O-methyl-O-desmethylangolensin. Two subjects produced a metabolite tentatively identified as 6-O-methyl-equol, and one subject produced daidzein as an additional metabolite of glycitein. These results show that glycitein is metabolized by human gut microorganisms and may follow metabolic pathways similar to other soy isoflavones.

The relationship between chemical structure and gut microbial degradation rates of 14 flavonoids, flavone, apigenin, chrysin, naringenin, kaempferol, genistein, daidzein, daidzin, puerarin, 7,4 '-dihydroxyflavone, 6,4 '-dihydroxyflavone, 5,4 '-dihydroxyflavone, 5,3 '-dihydroxyflavone, and 4 '-hydroxyflavone, was investigated by anaerobically fermenting the flavonoids with human gut microflora. (n = 11 subjects). Degradation rates for the 5,7,4 '-trihydroxyl flavonoids, apigenin, genistein, naringenin, and kaempferol, were significantly faster than the other structural motifs. Puerarin was resistant to degradation by the gut microflora. Extensive degradation of flavonoids by gut microflora. may result in lower overall bioavailability than those flavonoids that are slowly degraded because rapidly degrading flavonoids are less likely to be absorbed intact.

4-Methoxyresorcinol (3) was synthesized as the precursor for glycitein (6) synthesis by the oxidation of 3-hydroxy-4-methoxybenzaldehyde (1) to the aryl formate with H2O2 and a catalytic amount of SeO2. Glycitein (6) was synthesized by cyclization of 2,4,4'-trihydroxy-5-methoxydeoxybenzoin (5) with N,N-dimethylformamide, boron trifluoride diethyl ether, and methanesulfonyl chloride in a microwave oven.